Skip to main content
SpringerLink
Log in

Solar Flare Prediction and Feature Selection Using a Light-Gradient-Boosting Machine Algorithm

  • Research
  • Published:
Solar Physics Aims and scope Submit manuscript

Abstract

Solar flares are among the most severe space-weather phenomena, and they have the capacity to generate radiation storms and radio disruptions on Earth. The accurate prediction of solar-flare events remains a significant challenge, requiring continuous monitoring and identification of specific features that can aid in forecasting this phenomenon, particularly for different classes of solar flares. In this study, we aim to forecast C- and M-Class solar flares utilising a machine-learning algorithm, namely the Light Gradient Boosting Machine. We have utilised a dataset spanning nine years, obtained from the Space-weather Helioseismic and Magnetic Imager Active Region Patches (SHARP), with a temporal resolution of 1 h. A total of 37 flare features were considered in our analysis, comprising of 25 active-region parameters and 12 flare-history features. To address the issue of class imbalance in solar-flare data, we employed the Synthetic Minority Over-sampling Technique (SMOTE). We used two labelling approaches in our study: a fixed 24-h window label and a varying window that considers the changing nature of solar activity. Then, the developed machine-learning algorithm was trained and tested using forecast-verification metrics, with an emphasis on evaluating the true skill statistic (TSS). Furthermore, we implemented a feature-selection algorithm to determine the most significant features from the pool of 37 features that could distinguish between flaring and non-flaring active regions. We found that utilising a limited set of useful features resulted in improved prediction performance. For the 24-h prediction window, we achieved a TSS of 0.63 (0.69) and an accuracy of 0.90 (0.97) for ≥C- (≥M)-Class solar flares.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21

Similar content being viewed by others

References

  • Ahmadzadeh, A., Hostetter, M., Aydin, B., Georgoulis, M.K., Kempton, D.J., Mahajan, S.S., Angryk, R.: 2019, Challenges with extreme class-imbalance and temporal coherence: a study on solar flare data. In: 2019 IEEE International Conference on Big Data (Big Data), IEEE, Los Alamitos, 1423.

    Chapter  Google Scholar 

  • Arge, O.N., Pizzo, V.J.: 2000, Improvement in the prediction of solar wind conditions using near-real time solar magnetic field updates. J. Geophys. Res. Space Phys. 105, 10465. DOI.

    Article  ADS  Google Scholar 

  • Bloomfield, D.S., Higgins, P.A., McAteer, R.J., Gallagher, P.T.: 2012, Toward reliable benchmarking of solar flare forecasting methods. Astrophys. J. Lett. 747, L41. DOI.

    Article  ADS  Google Scholar 

  • Bobra, M.G., Couvidat, S.: 2015, Solar flare prediction using SDO/HMI vector magnetic field data with a machine-learning algorithm. Astrophys. J. 798, 135. DOI.

    Article  ADS  Google Scholar 

  • Bobra, M.G., Sun, X., Hoeksema, J.T., Turmon, M., Liu, Y., Hayashi, K., Barnes, G., Leka, K.D.: 2014, The Helioseismic and Magnetic Imager (HMI) vector magnetic field pipeline: SHARPs – Space-Weather HMI Active Region Patches. Solar Phys. 289, 3549. DOI.

    Article  ADS  Google Scholar 

  • Bussy-Virat, C.D., Ridley, A.J.: 2014, Predictions of the solar wind speed by the probability distribution function model. Space Weather 12, 337. DOI.

    Article  ADS  Google Scholar 

  • Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: 2002, SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 16, 321. DOI.

    Article  MATH  Google Scholar 

  • Chen, Y., Manchester, W.B., Hero, A.O., Toth, G., DuFumier, B., Zhou, T., Wang, X., Zhu, H., Sun, Z., Gombosi, T.I.: 2019, Identifying solar flare precursors using time series of SDO/HMI images and SHARP parameters. Space Weather 17, 1404. DOI.

    Article  ADS  Google Scholar 

  • Echer, E., Gonzalez, W.D., Guarnieri, F.L., Lago, A.D., Vieira, L.E.A.: 2005, Introduction to space weather. Adv. Space Res. 35, 855. DOI.

    Article  ADS  Google Scholar 

  • Falconer, D.A., Moore, R.L., Barghouty, A.F., Khazanov, I.: 2012, Prior flaring as a complement to free magnetic energy for forecasting solar eruptions. Astrophys. J. 757, 32. DOI.

    Article  ADS  Google Scholar 

  • Fisher, G.H., Bercik, D.J., Welsch, B.T., Hudson, H.S.: 2011, Global forces in eruptive solar flares: the Lorentz force acting on the solar atmosphere and the solar interior. Solar Phys. 277, 59. DOI.

    Article  ADS  Google Scholar 

  • Florios, K., Kontogiannis, I., Park, S.-H., Guerra, J.A., Benvenuto, F., Bloomfield, D.S., Georgoulis, M.K.: 2018, Forecasting solar flares using magnetogram-based predictors and machine learning. Solar Phys. 293, 28. DOI.

    Article  ADS  Google Scholar 

  • Han, H., Wang, W.-Y., Mao, B.-H.: 2005, Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: International Conference on Intelligent Computing, Springer, Berlin, 878.

    Google Scholar 

  • He, H., Garcia, E.A.: 2009, Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263. DOI.

    Article  Google Scholar 

  • He, H., Bai, Y., Garcia, E.A., Li, S.: 2008, ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 1322. DOI.

    Chapter  Google Scholar 

  • Huang, X., Wang, H.-N.: 2013, Solar flare prediction using highly stressed longitudinal magnetic field parameters. Res. Astron. Astrophys. 13, 351. DOI.

    Article  ADS  Google Scholar 

  • Jiao, Z., Sun, H., Wang, X., Manchester, W., Gombosi, T., Hero, A., Chen, Y.: 2020, Solar flare intensity prediction with machine learning models. Space Weather 18, e2020SW002440. DOI.

    Article  ADS  Google Scholar 

  • Jonas, E., Bobra, M., Shankar, V., Todd Hoeksema, J., Recht, B.: 2018, Flare prediction using photospheric and coronal image data. Solar Phys. 293, 48. DOI.

    Article  ADS  Google Scholar 

  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y.: 2017a, LightGBM: a highly efficient gradient boosting decision tree. In: Neural Information Processing Systems 30, 3149.

    Google Scholar 

  • Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y.: 2017b, Lightgbm: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems 30.

    Google Scholar 

  • LaBonte, B.J., Georgoulis, M.K., Rust, D.M.: 2007, Survey of magnetic helicity injection in regions producing X-class flares. Astrophys. J. 671, 955. DOI.

    Article  ADS  Google Scholar 

  • Leka, K.D., Barnes, G.: 2003, Photospheric magnetic field properties of flaring versus flare-quiet active regions. II. Discriminant analysis. Astrophys. J. 595, 1296. DOI.

    Article  ADS  Google Scholar 

  • Li, R., Zhu, J.: 2013, Solar flare forecasting based on sequential sunspot data. Res. Astron. Astrophys. 13, 1118. DOI.

    Article  ADS  Google Scholar 

  • Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: 2017, Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV).

    Google Scholar 

  • Liu, J.-F., Li, F., Wan, J., Yu, D.-R.: 2017, Short-term solar flare prediction using multi-model integration method. Res. Astron. Astrophys. 17, 034. DOI.

    Article  ADS  Google Scholar 

  • Liu, H., Liu, C., Wang, J.T.L., Wang, H.: 2019, Predicting solar flares using a long short-term memory network. Astrophys. J. 877, 121. DOI.

    Article  ADS  Google Scholar 

  • Liu, Y., Welsch, B.T., Valori, G., Georgoulis, M.K., Guo, Y., Pariat, E., Park, S.-H., Thalmann, J.K.: 2023, Changes of magnetic energy and helicity in solar active regions from major flares. Astrophys. J. 942, 27. DOI.

    Article  ADS  Google Scholar 

  • Mayank, P., Vaidya, B., Chakrabarty, D.: 2022, SWASTi-SW: space weather adaptive simulation framework for solar wind and its relevance to the Aditya-L1 mission. Astrophys. J. Suppl. Ser. 262, 23. DOI.

    Article  ADS  Google Scholar 

  • Moore, R.L., Falconer, D.A., Sterling, A.C.: 2012, The limit of magnetic-shear energy in solar active regions. Astrophys. J. 750, 24. DOI.

    Article  ADS  Google Scholar 

  • Nguyen, H.M., Cooper, E.W., Kamei, K.: 2011, Borderline over-sampling for imbalanced data classification. Int. J. Knowl. Eng. Soft Data Paradig. 3, 4.

    Article  Google Scholar 

  • Nishizuka, N., Sugiura, K., Kubo, Y., Den, M., Watari, S., Ishii, M.: 2017, Solar flare prediction model with three machine-learning algorithms using ultraviolet brightening and vector magnetograms. Astrophys. J. 835, 156. DOI.

    Article  ADS  Google Scholar 

  • Nishizuka, N., Sugiura, K., Kubo, Y., Den, M., Ishii, M.: 2018, Deep Flare Net (DeFN) model for solar flare prediction. Astrophys. J. 858, 113. DOI.

    Article  ADS  Google Scholar 

  • Odstrcil, D.: 2003, Modeling 3-D solar wind structure. Adv. Space Res. 32, 497. DOI.

    Article  ADS  Google Scholar 

  • Oughton, E.J., Hapgood, M., Richardson, G., Beggan, C., Thomson, M.W.P., Gibbs, M., Burnett, C., Gaunt, C.T., Trichas, M., Dada, R., Horne, R.B.: 2019, A risk assessment framework for the socioeconomic impacts of electricity transmission infrastructure failure due to space weather: an application to the United Kingdom. Risk Anal. 39, 1022. DOI.

    Article  Google Scholar 

  • Owens, M.J., Riley, P., Horbury, T.S.: 2017, Probabilistic solar wind and geomagnetic forecasting using an analogue ensemble or “Similar day” approach. Solar Phys. 292, 69. DOI.

    Article  ADS  Google Scholar 

  • Park, S.-h., Chae, J., Wang, H.: 2010, Productivity of solar flares and magnetic helicity injection in active regions. Astrophys. J. 718, 43. DOI.

    Article  ADS  Google Scholar 

  • Pomoell, J., Poedts, S.: 2018, EUHFORIA: European heliospheric forecasting information asset. J. Space Weather Space Clim. 8, A35. DOI.

    Article  ADS  Google Scholar 

  • Reiss, M.A., Temmer, M., Veronig, A.M., Nikolic, L., Vennerstrom, S., Schöngassner, F., Hofmeister, S.J.: 2016, Verification of high-speed solar wind stream forecasts using operational solar wind models. Space Weather 14, 495. DOI.

    Article  ADS  Google Scholar 

  • Ribeiro, F., Gradvohl, A.L.S.: 2021, Machine learning techniques applied to solar flares forecasting. Astron. Comput. 35, 100468. DOI.

    Article  ADS  Google Scholar 

  • Riley, P., Linker, J.A., Mikić, Z.: 2001, An empirically-driven global MHD model of the solar corona and inner heliosphere. J. Geophys. Res. Space Phys. 106, 15889. DOI.

    Article  ADS  Google Scholar 

  • Riley, P., Ben-Nun, M., Linker, J.A., Owens, M.J., Horbury, T.S.: 2017, Forecasting the properties of the solar wind using simple pattern recognition. Space Weather 15, 526. DOI.

    Article  ADS  Google Scholar 

  • Schrijver, C.J.: 2007, A characteristic magnetic field pattern associated with all major solar flares and its use in flare forecasting. Astrophys. J. 655, L117. DOI.

    Article  ADS  Google Scholar 

  • Sun, X.: 2019, The CGEM Lorentz Force Data from HMI Vector Magnetograms.

    Google Scholar 

  • Taylor, K.E.: 2001, Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res., Atmos. 106, 7183.

    Article  ADS  Google Scholar 

  • The SunPy Community, Mumford, S.J., Christe, S., Pérez-Suárez, D., Ireland, J., Shih, A.Y., Inglis, A.R., Liedtke, S., Hewett, R.J., Mayer, F., Hughitt, K., Freij, N., Meszaros, T., Bennett, S.M., Malocha, M., Evans, J., Agrawal, A., Leonard, A.J., Robitaille, T.P., Mampaey, B., Campos-Rozo, J.I., Kirk, M.S.: 2015, SunPy—Python for solar physics. Comput. Sci. Discov. 8, 014009. DOI.

    Article  Google Scholar 

  • Tóth, G., van der Holst, B., Huang, Z.: 2011, Obtaining potential field solutions with spherical harmonics and finite differences. Astrophys. J. 732, 102. DOI.

    Article  ADS  Google Scholar 

  • Wang, X., Chen, Y., Toth, G., Manchester, W.B., Gombosi, T.I., Hero, A.O., Jiao, Z., Sun, H., Jin, M., Liu, Y.: 2020, Predicting solar flares with machine learning: investigating solar cycle dependence. Astrophys. J. 895, 3. DOI.

    Article  ADS  Google Scholar 

Download references

Acknowledgments

We extend our sincere gratitude to the anonymous referee for the insightful comments and constructive suggestions, which have significantly enhanced the quality of this manuscript. PAV and PM would like to express their gratitude to Dr. Bhargav Vaidya for his dedicated and insightful discussions. PM would like to acknowledge the financial support provided by the Prime Minister’s Research Fellowship.

Author information

Authors and Affiliations

  1. Anton Pannekoek Institute for Astronomy, University of Amsterdam, Amsterdam, The Netherlands

    P. A. Vysakh

  2. Department of Physics, Indian Institute of Technology Dhanbad, Dhanbad, India

    P. A. Vysakh

  3. Department of Astronomy, Astrophysics and Space Engineering, Indian Institute of Technology Indore, Indore, India

    Prateek Mayank

Authors
  1. P. A. Vysakh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prateek Mayank
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P A Vysakh and P Mayank conceived, planned, and wrote the manuscript text. P A Vysakh developed the model, performed the computations, and generated figures. P Mayank supervised the project.

Corresponding author

Correspondence to P. A. Vysakh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vysakh, P.A., Mayank, P. Solar Flare Prediction and Feature Selection Using a Light-Gradient-Boosting Machine Algorithm. Sol Phys 298, 137 (2023). https://doi.org/10.1007/s11207-023-02223-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11207-023-02223-5

Keywords

Search

Navigation